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[jira] [Commented] (MAHOUT-1178) GSOC 2013: Improve Lucene support in Mahout

[Gokhan Capan (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1178?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13799916#comment-13799916
 ] 

Gokhan Capan commented on MAHOUT-1178:
--

Hi [~smarthi], 

Although I'm not sure if there is no more an interest, I have a Lucene matrix 
(in-memory) and a Solr matrix (that does not load the index into memory) 
implementations. I believe both can be committed after a couple review rounds.



> GSOC 2013: Improve Lucene support in Mahout
> ---
>
> Key: MAHOUT-1178
> URL: https://issues.apache.org/jira/browse/MAHOUT-1178
> Project: Mahout
>  Issue Type: New Feature
>Reporter: Dan Filimon
>  Labels: gsoc2013, mentor
> Fix For: Backlog
>
> Attachments: MAHOUT-1178.patch, MAHOUT-1178-TEST.patch
>
>
> [via Ted Dunning]
> It should be possible to view a Lucene index as a matrix.  This would
> require that we standardize on a way to convert documents to rows.  There
> are many choices, the discussion of which should be deferred to the actual
> work on the project, but there are a few obvious constraints:
> a) it should be possible to get the same result as dumping the term vectors
> for each document each to a line and converting that result using standard
> Mahout methods.
> b) numeric fields ought to work somehow.
> c) if there are multiple text fields that ought to work sensibly as well.
>  Two options include dumping multiple matrices or to convert the fields
> into a single row of a single matrix.
> d) it should be possible to refer back from a row of the matrix to find the
> correct document.  THis might be because we remember the Lucene doc number
> or because a field is named as holding a unique id.
> e) named vectors and matrices should be used if plausible.



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Re: Doubts regarding heuristic parameters for Neural networks

[Suneel Marthi]

There's already an implementation of Multi-layer Perceptron using BackProp in 
Mahout that's pending review and integration into Mahout trunk.

See https://issues.apache.org/jira/browse/MAHOUT-1265.

Yexi has documented the approach and the design in the JIRA ticket.

Unless what you are proposing is more efficient than what's been done by Yexi, 
we are repeating ourselves here.

Just a thought.





On Saturday, October 19, 2013 5:31 AM, surabhi pandey  
wrote:
 
Thanks for replying ted,as per our understanding you are trying to say this
needs to be done by the developer beforehand by using some  dynamic
techniques and while testing user will assign these values based on the
optimal values we have generated using some dynamic techniques is it
correct?


On Sat, Oct 19, 2013 at 11:57 AM, Ted Dunning  wrote:

> That has been the practice in Mahout so far.
>
> Generally, a higher level learner is used to adjust those parameters, but
> it is important for testing purposes to expose them.
>
>
> On Sat, Oct 19, 2013 at 6:16 AM, Sushanth Bhat(MT2012147) <
> sushanth.b...@iiitb.org> wrote:
>
> > Hi,
> >
> > We are implementing Multi-layer perceptron Neural networks using
> > back-propagation for Mahout. There are some parameters such as learning
> > rate, momentum, activation function, threshold error, number of layers,
> > number of neurons in hidden layers which are dependent upon the input
> data.
> > Are we suppose to make these parameters to be passed by user?
> >
> >
> > Thanks and regards,
> > Sushanth Bhat
> > IIIT-Bangalore
> >
>



-- 
Surabhi
http://www.linkedin.com/pub/surabhi-pandey/22/46/904

Re: Doubts regarding heuristic parameters for Neural networks

[surabhi pandey]

Thanks for replying ted,as per our understanding you are trying to say this
needs to be done by the developer beforehand by using some  dynamic
techniques and while testing user will assign these values based on the
optimal values we have generated using some dynamic techniques is it
correct?


On Sat, Oct 19, 2013 at 11:57 AM, Ted Dunning  wrote:

> That has been the practice in Mahout so far.
>
> Generally, a higher level learner is used to adjust those parameters, but
> it is important for testing purposes to expose them.
>
>
> On Sat, Oct 19, 2013 at 6:16 AM, Sushanth Bhat(MT2012147) <
> sushanth.b...@iiitb.org> wrote:
>
> > Hi,
> >
> > We are implementing Multi-layer perceptron Neural networks using
> > back-propagation for Mahout. There are some parameters such as learning
> > rate, momentum, activation function, threshold error, number of layers,
> > number of neurons in hidden layers which are dependent upon the input
> data.
> > Are we suppose to make these parameters to be passed by user?
> >
> >
> > Thanks and regards,
> > Sushanth Bhat
> > IIIT-Bangalore
> >
>



-- 
Surabhi
http://www.linkedin.com/pub/surabhi-pandey/22/46/904